Fig 1.
Monthly variation of air temperature,
water temperature & % weight gain. Air temperature & water temperature (panel a) and water temperature & % weight gain (panel b).
Table 1.
Descriptive statistics of length and weight of Tilapia broodfish during study period.
Fig 2.
Growth pattern, FCR and SGR of tilapia.
(Panel a) exponential trend of fish length, (panel b) exponential trend of fish weight, (panel c) Changing pattern of percent length gain & percent weight gain, (panel d) Changing pattern of SGR & FCR.
Table 2.
Pearson correlations between Tilapia broodfish growth and water quality parameters.
Fig 3.
Data stationarity checking of tilapia broodfish growth.
(Panel a) trend analysis plot for weight, stationary, (panel b) trend analysis plot for % weight gain, non-stationary, (panel c) normal probability plot of tilapia weight by ADF test, stationary, observations clustered within a low percentile region of the ADF test statistic’s distribution, (panel d) normal probability plot of % weight gain by ADF test, non-stationarity, a substantial number of observations outside the critical region, (panel e) normal histogram of weight, stationary as the data exhibits normal distribution and has a distinctive peak in the center, gradually tapering off, (panel f) normal histogram of % weight gain, non-stationary as it did not exhibit a typical distinct peak in the center and a gradual tapering off.
Fig 4.
The ACF and PACF of stationarity checking of growth data.
(Panels a & b) ACF & PACF of weight with 5% significance limit showed stationary pattern as the plots indicating a rapid decline in correlations, weakening ACF associations with greater lags, and significant PACF spikes primarily at the initial lags. (panels c & d) ACF & PACF of % weight gain with 5% significance limit, showed non-stationary, as the ACF demonstrated a gradual decrease in correlations, with evident connections between the series and its lags, and significant PACF peaks at various lags, suggesting the presence of trends or seasonality.
Fig 5.
Stationarity of transformed data for tilapia % weight gain.
(Panel a) Box-Cox plot where value of λ=1, (panel b) probability plot distribution, support the significant value (p > 0.05), (panel c) normal probability plot by ADF test, observations clustered within a low percentile region of test statistic’s distribution, (panel d) normal histogram, normal bell-shaped distribution and has a distinctive peak in the center, gradually tapering off.
Fig 6.
The ACF and PACF of stationary data % weight gain.
(Panel a) ACF with 5% significance limit, (panel b) PACF with 5% significance limit.
Table 3.
Suitable model fitting for tilapia broodfish weight data. The model is accepted with minimum BIC and AIC (*) value and considerable other values for the models.
Table 4.
Suitable model fitting for tilapia percent weight gain data. The model is accepted with minimum BIC and AIC (*) value and considerable other values for the models.
Fig 7.
Residual ACF and PACF with normal spike distribution tilapia broodfish growth.
Length (panels a & b) and weight (panels c & d).
Table 5.
Cross correlation of percent weight gain & water temperature and percent weight gain & solar intensity.
Table 6.
Forecasting of tilapia weight from February 2024 to January 2027 with ARIMA.
Fig 8.
Forecasting the tilapia broodfish weight up to the end of January 2027 with ARIMA.
Table 7.
Forecasting of tilapia percent weight gain from February 2024 to January 2027 with ARIMAX.
Fig 9.
Percent weight gain forecasting.
Forecasting the tilapia broodfish percent weight gain up to the end of January 2027 with ARIMAX.
Table 8.
Simulation of percent weight gain in 10 series up to the end of January 2027 with ARIMAX.
Fig 10.
Percent weight gain simulation.
Simulation of percent weight gain up to the end of January 2027 with ARIMAX.